Aspects of the disclosure relate to monitoring devices at enterprise locations using machine-learning models to protect enterprise-managed information and resources. In some embodiments, a computing platform may receive, from one or more data source computer systems, passive monitoring data. Based on applying a machine-learning classification model to the passive monitoring data received from the one or more data source computer systems, the computing platform may determine to trigger a data capture process at an enterprise center. In response to determining to trigger the data capture process, the computing platform may initiate an active monitoring process to capture event data at the enterprise center. Thereafter, the computing platform may generate one or more alert messages based on the event data captured at the enterprise center. Then, the computing platform may send the one or more alert messages to one or more enterprise computer systems.

A reinforcement learning method for frame-level bit allocation is disclosed. The reinforcement learning method includes steps of: (a) at a testing time, computing a state according to a plurality of features; (b) determining an action according to a policy; (c) determining a number of bits allocated to an i-th frame in a group of pictures (GOP) according to the action, a GOP-level bit budget and the state, wherein i is a positive integer; (d) encoding the i-th frame according to the number of bits allocated to the i-th frame in the GOP; and (e) repeating the steps (a)˜(d) until an end of the GOP.

A system for detecting license plates is described. The system receives raw data comprising images of license plates. A base version of a ground truth is prepared based on the raw data, using a generic license plate detection (LPD). The system prepares input data for training a deep learning network. The deep learning network is trained with the prepared input data. A newly trained generic (LPD) is formed using data generated by the existing generic (LPD).

Image data is obtained about an area that includes a plurality of sub-areas. One of the sub-areas is selected as a destination sub-area based on the destination sub-area being unoccupied. Then, upon detecting a candidate marker for the destination sub-area, the image data including the candidate marker is provided to a remote computer. A vehicle is operated to a stop in the destination sub-area. Then, upon receiving a message from the remote computer specifying an availability of the destination sub-area based at least on the image data, the vehicle is maintained in the destination sub-area or the vehicle is operated out of the destination sub-area.

A system includes an expert graphical user interface configured to: present a list of reliability measures of an industrial machine, facilitate a selection by a user of a reliability measure from the list of reliability measures, present a representation of a smart band data collection template associated with the reliability measure selected by the user, and a data routing and collection system configured to, in response to a user indication of acceptance of the smart band data collection template, collect data from a plurality of sensors in an industrial environment in response to a data value from one of the plurality of sensors being detected outside of an acceptable range of data values.

Provided are an information processing apparatus, an information processing method, and a program capable of accumulating appropriate relearning data. An information processing apparatus includes an input unit that inputs input data to a learned model acquired in advance through machine learning using learning data, an acquisition unit that acquires output data output from the learned model through the input using the input unit, a reception unit that receives correction performed by a user for the output data acquired by the acquisition unit, and a storage controller that performs control for storing, as relearning data of the learned model, the input data and the output data that reflects the correction received by the reception unit in a storage unit in a case where a value indicating a correction amount acquired by performing the correction for the output data is equal to or greater than a threshold value.

Systems, methods, and non-transitory computer readable media are provided for displaying and annotating map-based geolocation data at an augmented reality (AR) headset. Users with access to the map-based geolocation data can create or confirm annotations for geospatial data that may be sent to the server computer and transmitted back to the headset of the user as well as different AR headsets associated with other users.

A neural network in multi-task deep learning paradigm for machine vision includes an encoder that further includes a first, a second, and a third tier. The first tier comprises a first-tier unit having one or more first-unit blocks. The second tier receives a first-tier output from the first tier at one or more second-tier units in the second tier, a second-tier unit comprises one or more second-tier blocks, the third tier receives a second-tier output from the second tier at one or more third-tier units in the third tier, and a third-tier block comprises one or more third-tier blocks. The neural network further comprises a decoder operatively the encoder to receive an encoder output from the encoder as well as one or more loss function layers that are configured to backpropagate one or more losses for training at least the encoder of the neural network in a deep learning paradigm.

An x-ray image laterality detection system is provided. The x-ray image laterality detection system includes a detection computing device. The processor of the computing device is programmed to execute a neural network model for analyzing x-ray images, wherein the neural network model is trained with training x-ray images as inputs and observed laterality classes associated with the training x-ray images as outputs. The process is also programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign a laterality class to the unclassified x-ray image. If the assigned laterality class is not target laterality, the processor is programmed to adjust the unclassified x-ray image to derive a corrected x-ray image having the target laterality and output the corrected x-ray image. If the assigned laterality class is the target laterality, the processor is programmed to output the unclassified x-ray image.

Systems and methods for predicting user interaction with vehicles. A computing device receives an image and a video segment of a road scene, the first at least one of an image and a video segment being taken from a perspective of a participant in the road scene and then generates stimulus data based on the image and the video segment. Stimulus data is transmitted to a user interface and response data is received, which includes at least one of an action and a likelihood of the action corresponding to another participant in the road scene. The computing device aggregates a subset of the plurality of response data to form statistical data and a model is created based on the statistical data. The model is applied to another image or video segment and a prediction of user behavior in the another image or video segment is generated.